基于卷积神经网络的核电多相无刷励磁系统旋转整流器故障诊断

doi:10.19595/j.cnki.1000-6753.tces.221531

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Abstract

Multi-phase brushless excitation system is widely used in large-capacity nuclear power plants. The safe and stable operation of nuclear power plants depends on the accurate diagnosis of rotating rectifier fault. However, the existing methods cannot accurately distinguish all types of faults, which makes it difficult to meet the demands in practice. Recently, as a representative artificial intelligence method, deep learning provided new ideas for solving this problem. Therefore, this paper develops a fault diagnosis scheme for the rotating rectifier based on convolutional neural network.
First, taking an 11-phase brushless excitation system as an example, the excitation current characteristics after the rotating rectifier fault is analyzed. The results show that the excitation current waveforms contain amplitude and phase information of all fault harmonic, so that they can be various under different fault modes and used for fault diagnosis of the rotating rectifier.
Then, a one-dimensional dilated convolutional neural network (1D-DCNN) model that can realize the fault diagnosis is introduced. The 1D-DCNN establishes a mapping relationship from the input (excitation current waveforms) to the output (fault mode). Meanwhile, the difference between the largest probability and the second largest probability of the output is defined as the confidence based on the conditional probability of samples belonging to different fault modes given by the model, which is used to measure the reliability of the diagnosis results. The process of fault diagnosis based on 1D-DCNN model includes three stages. At stage 1, we acquire raw data at several voltage levels to ensure that the model has applicability and generalization ability to different voltage levels. Then, we need to preprocess the data in terms of sliding window segmentation, filtering and mean normalization. At stage 2, the 1D-DCNN model is trained and used for online diagnosis of rotating rectifier faults. At stage 3, the Score-CAM method is introduced to clarify the interpretability of the 1D-DCNN model.
Finally, the effectiveness of the proposed 1D-DCNN model is verified by the prototype experiment. We mainly study diode open-circuit faults of 11-phase brushless excitation system which include eight fault types. The processed samples are divided into training set, verification set and test set. The verification set is utilized to determine the hyperparameter of the model. We use the control variable method to optimize the hyperparameters of each layer in turn. Next, the Score-CAM method is used to provide interpretability for the 1D-DCNN model and explain how hyperparameters influence fault diagnosis performance. Besides, the test sets with noise are added as a comparison. We compare the diagnosis accuracy of different models in the original test set and the test sets with noise, and the 1D-DCNN model shows better anti-noise ability. In addition, we analyze the confidence of model diagnosis results in the original test set and the test sets with noise. The results show that selecting the appropriate confidence threshold contribute to improve the classification accuracy of the model.
In conclusion, the feasibility of fault diagnosis of rotating rectifiers based on convolutional neural network is verified, which provides a new data-driven idea for fault diagnosis of rotating rectifier.

Key words： Brushless excitation system rotating rectifier deep learning convolutional neural network fault diagnosis

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TM341

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	Liang Zhengqiu
	Hao Liangliang
	Zhou Yanzhen
	Duan Xianwen
	Wang Guang

Cite this article:

Liang Zhengqiu,Hao Liangliang,Zhou Yanzhen等. Fault Diagnosis of Rotating Rectifier in Nuclear Multi-phase Brushless Excitation System Based on Convolutional Neural Network[J]. Transactions of China Electrotechnical Society, 0, (): 20235406-20235406.

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